A multi‐metric movement model for identifying elk parturition events

Abstract

For wildlife managers, identifying the time and location of ungulate parturition events is necessary to protect parturition habitat and inform population models. Current methods for identifying parturition events include neonatal capture and monitoring and visual analysis of location data, which are resource intensive and subject to biases. Recently, movement‐based analyses using the spatial and temporal patterns contained in location data have shown promise for identifying parturition behavior; however, questions regarding efficacy and best practice have limited the adoption of such movement‐based parturition models into management programs. We used location data collected between 2017−2018 from a California population of female Roosevelt elk with known parturition status to build a set of movement‐based models. We compared each model's ability to accurately classify individual's parturition status and identify the timing of parturition. The top model showed that within 72 hours of a parturition event, elk made smaller movements, had smaller home ranges, and had longer visits to individual locations. Of the 16 individuals in our study with known parturition status, our approach to classifying an individual's status was 81% accurate in cross‐validation, which increased to 100% accuracy when the complete dataset was used to train the top model. We then used the top, global and single‐metric models to establish probability thresholds and identify parturition events for collared‐elk with unknown parturition status. Our results align with a growing number of studies that have shown movement‐based parturition models are a reliable method that could augment or replace field‐based techniques for identifying parturition events and quantifying parturition rates in ungulate populations.

Evaluating movement-based methods for estimating the frequency and timing of parturition in mule deer

“…Due to these behavioral differences, methods developed for specific ungulates may not be as effective when transferring across species and some modification may be required [25,32,33]. Furthermore, methods using multiple movement metrics may be more effective than those using a single metric [34][35][36].…”

Using piecewise regression to identify biological phenomena in biotelemetry datasets

“…Patterns in movement data thus offer a cost‐effective means for identifying parturition in radio‐collared ungulates (DeMars et al, 2013; Nicholson et al, 2019; Peterson et al, 2018). Although multiple methods have been used to infer parturition events, many are sensitive to parameter choices or involve visual observation of movement metrics, which can be time‐intensive (Dettki & Ericsson, 2008; Mohr et al, 2022).…”

“…Patterns in movement data thus offer a cost-effective means for identifying parturition in radio-collared ungulates(DeMars et al, 2013;Nicholson et al, 2019;Peterson et al, 2018). Although multiple methods have been used to infer parturition events, many are sensitive to parameter choices or involve visual observation of movement metrics, which can be time-intensive(Dettki & Ericsson, 2008;Mohr et al, 2022).To demonstrate remote identification of parturition, we provide an example of quantifying movements of a pregnant moose (Figure2).Severud et al (2015) confirmed the parturition period by locating the twin calves shortly after their birth. In this case, the first segment is modelled with an intercept and slope of zero, representing typical moose movement, and the second segment is modelled with a change in the mean displacement (i.e.…”